The current broadcast model of television programs, and radio programs for that matter, provides no context oriented means of interaction with members of the audience. The producers of such programs have limited resources (e.g., primarily the time and expertise to delve to extended depths in all topic areas), and thus they generate programs suitable for the greatest number of viewers or listeners in their target audience, and one that fits within the time constraints of medium (e.g., television). All members of the audience, however, are not identical in their information needs and interests.
As an example, television broadcast news is one type of program for which at least some viewers may find themselves wanting for more information (or content). That is, news is insufficient for conveying complete and useful information to all television viewers, since the information needs or wants of all viewers differ. For the most part, a person's information needs are grounded in his or her context or task. Not only will such contexts and tasks differ among viewers, they may also differ for a given viewer over time. However, current systems and methods of presenting programs via television and radio, as examples, treat such programs as an end product. They offer no way for a viewer to quickly gain access to related or collateral information.
Research in the indexing and retrieval of broadcast news and in the retrieval of information relevant and useful to viewers of broadcast news has primarily focused on news summarization, the generation of personalized news broadcasts (i.e., “personalcasts”), and providing news on demand (NOD), see Brown, M. G., Foote, J. T., Jones, G. J. F., Sparck Jones, K., and Young, S. J., Automatic Content-based Retrieval of Broadcast News, Proceedings of ACM Multimedia. San Francisco, pp. 35-43, (1995); Maybury, M., News on Demand, Communications of the ACM, 43(2), pp. 33-34, (February 2000) (hereinafter “Maybury”); and Wilcox, L., and Boreczky, J. S.:, Annotation and Segmentation for Multimedia Indexing and Retrieval, HICSS, pp. 259-266, (1998). Also, related to this work are automated question and answer systems, which attempt to provide synthesized answers to user queries, see Light, M., Maybury, M., Personalized Multimedia Information Access, Communications of the ACM, 45(5), pp. 54-59, (May 2002). A survey of much of this work can be found in Maybury.
These systems generally require explicit interaction by the user to attempt to communicate his or her context to the system or to know her needs up front and provide queries for the information of interest, so they are not particularly efficient from the user's perspective. However, the user often does not know the information that he or she wants ahead of time, and even if he or she does, communicating with an information retrieval (IR) engine is a task that is difficult to do proficiently, even for advanced users. In addition, as previously discussed, news stories that discuss a topic of interest to the viewer likely do not go into sufficient detail, and the use of many traditional IR systems by the user to retrieve additional information is subject to information overload. For example, use of Internet search engines, such as Google, to find expanded and useful information often result in too numerous potentially matching documents. Typically, many of these documents are similar, but not necessarily useful to the user.